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Optimal Scheduling Strategies for EV Charging and Discharging in a Coupled Power–Transportation Network with V2G Scheduling and Dynamic Pricing

Author

Listed:
  • Yunzheng Ran

    (College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China)

  • Honghua Liao

    (College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China)

  • Huijun Liang

    (College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China)

  • Luoping Lu

    (College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China)

  • Jianwei Zhong

    (College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China)

Abstract

With the increasing penetration of electric vehicles (EVs), the spatial–temporal coupling between the transportation network (TN) and the power distribution network (PDN) has intensified greatly. Large-scale uncoordinated charging of EVs significantly impacts both the PDN and TN. In this paper, an optimal scheduling strategy for EV charging and discharging in a coupled power–transportation network (CPTN) with Vehicle-to-Grid (V2G) scheduling and dynamic pricing is proposed. The strategy considers the influence of dynamic transportation road network (DTRN) information on EV driving patterns, as well as the unique vehicle characteristics and mobile energy storage capabilities of EVs. Firstly, a DTRN model is established. Subsequently, the dynamic Dijkstra algorithm is utilized to accurately simulate the EV driving paths and predict the spatial–temporal distribution of the EV charging load. Secondly, optimal scheduling for EV charging and discharging within the CPTN is performed, guided by a V2G model coupled with a multi-time dynamic electricity price (MTDEP) strategy to optimize the grid load curve while accommodating the charging requirements of EVs. Finally, the effectiveness and superiority of the proposed optimization scheduling model are validated by the IEEE 33-node PDN test system.

Suggested Citation

  • Yunzheng Ran & Honghua Liao & Huijun Liang & Luoping Lu & Jianwei Zhong, 2024. "Optimal Scheduling Strategies for EV Charging and Discharging in a Coupled Power–Transportation Network with V2G Scheduling and Dynamic Pricing," Energies, MDPI, vol. 17(23), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:6167-:d:1538555
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    References listed on IDEAS

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    1. Sizu Hou & Xinyu Zhang & Haiqing Yu, 2024. "Electric Vehicle Charging Load Prediction Considering Spatio-Temporal Node Importance Information," Energies, MDPI, vol. 17(19), pages 1-14, September.
    2. Mu, Yunfei & Wu, Jianzhong & Jenkins, Nick & Jia, Hongjie & Wang, Chengshan, 2014. "A Spatial–Temporal model for grid impact analysis of plug-in electric vehicles," Applied Energy, Elsevier, vol. 114(C), pages 456-465.
    3. Yu, Hang & Shang, Yitong & Niu, Songyan & Cheng, Chong & Shao, Ziyun & Jian, Linni, 2022. "Towards energy-efficient and cost-effective DC nanaogrid: A novel pseudo hierarchical architecture incorporating V2G technology for both autonomous coordination and regulated power dispatching," Applied Energy, Elsevier, vol. 313(C).
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